OpenRaiser/Intern-Atlas huggingface dataset:
A simple en,ex marking rule for degenerate intersection points in 2D polygon clipping
Venue: arXiv cs.CG 2018
Year: 2018

A simple en,ex rule to mark the intersection points of 2D input polygon contours separating the polygon interior from its exterior in the vicinity of the intersections is presented. Its form is close to the original Greiner & Hormann algorithm rule but encompasses degenerate intersections that are not self-intersections. It only uses local geometric information once the hand of the two input contours is known. The approach foundation is the distinction between two features of the studied intersections: the geometric intersection point and the assembling/concatenation point of the result contour/border. No special form of the intersection finding procedure is required.
Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB
Venue: arXiv cs.CV 2018
Year: 2018

Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 33.2% and a Relative RMSE drop of 54.0% on the ICVL natural hyperspectral image dataset.
RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning
Venue: arXiv cs.RO 2018
Year: 2018

Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT.
Optimal Net-Load Balancing in Smart Grids with High PV Penetration
Venue: arXiv cs.DS 2018
Year: 2018

Mitigating Supply-Demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as Demand Response (DR) have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for Smart Grids with high PV penetration. These grids can sometimes exhibit supply surplus causing over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are complex and computationally expensive. This increases the complexity of net-load balancing systems used by the grid operator and limits their scalability. Recently new technologies have been developed that enable the rapid and selective connection of PV modules of an installation to the grid. Taking advantage of these advancements, we develop a unified optimal net-load balancing framework which performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and develop bounded approximation algorithms for minimizing the curtailment cost. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation. We also incorporate the notion of fairness to ensure that curtailment is evenly distributed among all the nodes. Finally, we develop an online algorithm which performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions which are close to optimal in a small amount of time.
An accurate finite element method for the numerical solution of isothermal and incompressible flow of viscous fluid
Venue: arXiv cs.CE 2018
Year: 2018

Despite its numerical challenges, finite element method is used to compute viscous fluid flow. A consensus on the cause of numerical problems has been reached; however, general algorithms---allowing a robust and accurate simulation for any process---are still missing. Either a very high computational cost is necessary for a direct numerical solution (DNS) or some limiting procedure is used by adding artificial dissipation to the system. These stabilization methods are useful; however, they are often applied relative to the element size such that a local monotonous convergence is challenging to acquire. We need a computational strategy for solving viscous fluid flow using solely the balance equations. In this work, we present a general procedure solving fluid mechanics problems without use of any stabilization or splitting schemes. Hence, its generalization to multiphysics applications is straightforward. We discuss emerging numerical problems and present the methodology rigorously. Implementation is achieved by using open-source packages and the accuracy as well as the robustness is demonstrated by comparing results to the closed-form solutions and also by solving well-known benchmarking problems.
Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality
Venue: arXiv cs.RO 2018
Year: 2018

We address the problem of interactively controlling the workspace of a mobile robot to ensure a human-aware navigation. This is especially of relevance for non-expert users living in human-robot shared spaces, e.g. home environments, since they want to keep the control of their mobile robots, such as vacuum cleaning or companion robots. Therefore, we introduce virtual borders that are respected by a robot while performing its tasks. For this purpose, we employ a RGB-D Google Tango tablet as human-robot interface in combination with an augmented reality application to flexibly define virtual borders. We evaluated our system with 15 non-expert users concerning accuracy, teaching time and correctness and compared the results with other baseline methods based on visual markers and a laser pointer. The experimental results show that our method features an equally high accuracy while reducing the teaching time significantly compared to the baseline methods. This holds for different border lengths, shapes and variations in the teaching process. Finally, we demonstrated the correctness of the approach, i.e. the mobile robot changes its navigational behavior according to the user-defined virtual borders.
Maximum Secrecy Throughput of MIMOME FSO Communications with Outage Constraints
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we consider a scenario where two multiple-aperture legitimate nodes (Alice and Bob) communicate by means of Free-Space Optical (FSO) communication in the presence of a multiple-aperture eavesdropper (Eve), which is subject to pointing errors. Two different schemes are considered depending on the availability of channel state information (CSI) at Alice: i) the adaptive scheme, where Alice possesses the instantaneous CSI with respect to Bob; ii) the fixed-rate scheme, where such information is not available at Alice. The performance of the aforementioned schemes is evaluated in terms of a recently proposed metric named effective secrecy throughput (EST), which encompasses both the reliability and secrecy constraints. By constraining the system to operate below a given maximum allowed secrecy outage probability, we evaluate the EST analytically and through numerical results, showing that the use of multiple apertures at Alice is very important towards achieving the optimal EST.
To Learn or Not to Learn Features for Deformable Registration?
Venue: arXiv cs.CV 2018
Year: 2018

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights into the type of DL framework useful for feature learning and the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.
Cross-Media Similarity Evaluation for Web Image Retrieval in the Wild
Venue: arXiv cs.CV 2018
Year: 2018

In order to retrieve unlabeled images by textual queries, cross-media similarity computation is a key ingredient. Although novel methods are continuously introduced, little has been done to evaluate these methods together with large-scale query log analysis. Consequently, how far have these methods brought us in answering real-user queries is unclear. Given baseline methods that compute cross-media similarity using relatively simple text/image matching, how much progress have advanced models made is also unclear. This paper takes a pragmatic approach to answering the two questions. Queries are automatically categorized according to the proposed query visualness measure, and later connected to the evaluation of multiple cross-media similarity models on three test sets. Such a connection reveals that the success of the state-of-the-art is mainly attributed to their good performance on visual-oriented queries, while these queries account for only a small part of real-user queries. To quantify the current progress, we propose a simple text2image method, representing a novel test query by a set of images selected from large-scale query log. Consequently, computing cross-media similarity between the test query and a given image boils down to comparing the visual similarity between the given image and the selected images. Image retrieval experiments on the challenging Clickture dataset show that the proposed text2image compares favorably to recent deep learning based alternatives.
Distributed second order methods with increasing number of working nodes
Venue: arXiv cs.IT 2018
Year: 2018

Recently, an idling mechanism has been introduced in the context of distributed \emph{first order} methods for minimization of a sum of nodes' local convex costs over a generic, connected network. With the idling mechanism, each node $i$, at each iteration $k$, is active -- updates its solution estimate and exchanges messages with its network neighborhood -- with probability $p_k$, and it stays idle with probability $1-p_k$, while the activations are independent both across nodes and across iterations. In this paper, we demonstrate that the idling mechanism can be successfully incorporated in \emph{distributed second order methods} also. Specifically, we apply the idling mechanism to the recently proposed Distributed Quasi Newton method (DQN). We first show theoretically that, when $p_k$ grows to one across iterations in a controlled manner, DQN with idling exhibits very similar theoretical convergence and convergence rates properties as the standard DQN method, thus achieving the same order of convergence rate (R-linear) as the standard DQN, but with significantly cheaper updates. Simulation examples confirm the benefits of incorporating the idling mechanism, demonstrate the method's flexibility with respect to the choice of the $p_k$'s, and compare the proposed idling method with related algorithms from the literature.
Predicting Visual Features from Text for Image and Video Caption Retrieval
Venue: arXiv cs.CV 2018
Year: 2018

This paper strives to find amidst a set of sentences the one best describing the content of a given image or video. Different from existing works, which rely on a joint subspace for their image and video caption retrieval, we propose to do so in a visual space exclusively. Apart from this conceptual novelty, we contribute \emph{Word2VisualVec}, a deep neural network architecture that learns to predict a visual feature representation from textual input. Example captions are encoded into a textual embedding based on multi-scale sentence vectorization and further transferred into a deep visual feature of choice via a simple multi-layer perceptron. We further generalize Word2VisualVec for video caption retrieval, by predicting from text both 3-D convolutional neural network features as well as a visual-audio representation. Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and the very recent NIST TrecVid challenge for video caption retrieval detail Word2VisualVec's properties, its benefit over textual embeddings, the potential for multimodal query composition and its state-of-the-art results.
A Maximal Heterogeneity Based Clustering Approach for Obtaining Samples
Venue: arXiv cs.LG 2018
Year: 2018

Medical and social sciences demand sampling techniques which are robust, reliable, replicable and have the least dissimilarity between the samples obtained. Majority of the applications of sampling use randomized sampling, albeit with stratification where applicable. The randomized technique is not consistent, and may provide different samples each time, and the different samples themselves may not be similar to each other. In this paper, we introduce a novel non-statistical no-replacement sampling technique called Wobbly Center Algorithm, which relies on building clusters iteratively based on maximizing the heterogeneity inside each cluster. The algorithm works on the principle of stepwise building of clusters by finding the points with the maximal distance from the cluster center. The obtained results are validated statistically using Analysis of Variance tests by comparing the samples obtained to check if they are representative of each other. The obtained results generated from running the Wobbly Center algorithm on benchmark datasets when compared against other sampling algorithms indicate the superiority of the Wobbly Center Algorithm.
ML and Near-ML Decoding of LDPC Codes Over the BEC: Bounds and Decoding Algorithms
Venue: arXiv cs.IT 2018
Year: 2018

The performance of maximum-likelihood (ML) decoding on the binary erasure channel for finite-length low-density parity-check (LDPC) codes from two random ensembles is studied. The theoretical average spectrum of the Gallager ensemble is computed by using a recurrent procedure and compared to the empirically found average spectrum for the same ensemble as well as to the empirical average spectrum of the Richardson-Urbanke ensemble and spectra of selected codes from both ensembles. Distance properties of the random codes from the Gallager ensemble are discussed. A tightened union-type upper bound on the ML decoding error probability based on the precise coefficients of the average spectrum is presented. A new upper bound on the ML decoding performance of LDPC codes from the Gallager ensemble based on computing the rank of submatrices of the code parity-check matrix is derived. A new low-complexity near-ML decoding algorithm for quasi-cyclic LDPC codes is proposed and simulated. Its performance is compared to the upper bounds on the ML decoding performance.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Venue: arXiv cs.CR 2018
Year: 2018

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through the models' structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its influence, attribute inference attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks and begins to shed light on what other factors may be in play. Finally, we explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks.
Modeling Normative Multi-Agent Systems from a Kelsenian Perspective
Venue: arXiv cs.LO 2018
Year: 2018

Standard Deontic Logic (SDL) has been used as the underlying logic to model and reason over Multi-Agent Systems governed by norms (NorMAS). It is known that SDL is not able to represent contrary-to-duty (CTD) scenarios in a consistent way. That is the case, for example, of the so-called Chisholm paradox, which models a situation in which a conditional obligation that specifies what must be done when a primary obligation is violated holds. In SDL, the set of sentences that represent the Chisholm paradox derives inconsistent sentences. Due to the autonomy of the software agents of a NorMAS, norms may be violated and the underlying logic used to model the NorMAS should be able to represent violation scenarios. The contribution of this paper is threefold: (i) we present how Kelsenian thinking, from his jurisprudence in the context of legal ontologies, and Intuitionist Hybrid Logic can be adopted in the modeling of NorMAS, (ii) discuss how this approach overcomes limitations of the SDL and (iii) present a discussion about normative conflict identification according to Hill's functional taxonomy, that generalizes from standard identification by impossibility-of-joint-compliance test.
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
Venue: arXiv cs.RO 2018
Year: 2018

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.
Structurally Parameterized d-Scattered Set
Venue: arXiv cs.CC 2018
Year: 2018

In $d$-Scattered Set we are given an (edge-weighted) graph and are asked to select at least $k$ vertices, so that the distance between any pair is at least $d$, thus generalizing Independent Set. We provide upper and lower bounds on the complexity of this problem with respect to various standard graph parameters. In particular, we show the following: - For any $d\ge2$, an $O^*(d^{\textrm{tw}})$-time algorithm, where $\textrm{tw}$ is the treewidth of the input graph. - A tight SETH-based lower bound matching this algorithm's performance. These generalize known results for Independent Set. - $d$-Scattered Set is W[1]-hard parameterized by vertex cover (for edge-weighted graphs), or feedback vertex set (for unweighted graphs), even if $k$ is an additional parameter. - A single-exponential algorithm parameterized by vertex cover for unweighted graphs, complementing the above-mentioned hardness. - A $2^{O(\textrm{td}^2)}$-time algorithm parameterized by tree-depth ($\textrm{td}$), as well as a matching ETH-based lower bound, both for unweighted graphs. We complement these mostly negative results by providing an FPT approximation scheme parameterized by treewidth. In particular, we give an algorithm which, for any error parameter $ε> 0$, runs in time $O^*((\textrm{tw}/ε)^{O(\textrm{tw})})$ and returns a $d/(1+ε)$-scattered set of size $k$, if a $d$-scattered set of the same size exists.
Status Updates Through Multicast Networks
Venue: arXiv cs.IT 2018
Year: 2018

Using age of information as the freshness metric, we examine a multicast network in which real-time status updates are generated by the source and sent to a group of $n$ interested receivers. We show that in order to keep the information freshness at each receiver, the source should terminate the transmission of the current update and start sending a new update packet as soon as it receives the acknowledgements back from any $k$ out of $n$ nodes. As the source stopping threshold $k$ increases, a node is more likely to get the latest generated update, but the age of the most recent update is more likely to become outdated. We derive the age minimized stopping threshold $k$ that balances the likelihood of getting the latest update and the freshness of the latest update for shifted exponential link delay. Through numerical evaluations for different stopping strategies, we find that waiting for the acknowledgements from the earliest $k$ out of $n$ nodes leads to lower average age than waiting for a pre-selected group of $k$ nodes. We also observe that a properly chosen threshold $k$ can prevent information staleness for increasing number of nodes $n$ in the multicast network.
TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow
Venue: arXiv cs.LG 2018
Year: 2018

We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel without interference of the global interpreter lock. As part of this project, we introduce BatchPPO, an efficient implementation of the proximal policy optimization algorithm. By open sourcing TensorFlow Agents, we hope to provide a flexible starting point for future projects that accelerates future research in the field.